Progressive Physics-Driven Deep Conversion Of sRGB to Raw Images

Raw images are commonly processed by image signal processing (ISP) algorithms to standard RGB (sRGB) images in order to save the storage space and provide a suitable format for human visual system. Distortion and information loss are introduced in this procedure which degrades the performance of following tasks with these sRGB images as the input. To leverage the benefits from raw images, reversing sRGB images to raw images is expected. In this paper, a progressive physics-driven deep learning algorithm is proposed for the conversion of sRGB images to higher quality raw images by fusing physical-driven and data-driven deep learning approaches. Based on an observation that deep convolution neural networks (CNNs) are biased towards learning low-frequency functions, the proposed framework includes a high-frequency aware guidance branch to provide progressive guidance for the reconstruction branch. Experimental results indicate that the proposed algorithm improves reconstruction quality of existing data-driven approaches. It also reduces the sensitivity of existing data-driven approaches to test data in the sense that those images that are hard to be restored by the existing approaches are improved much more.